The Bayesian Decision Tree Technique with a Sweeping Strategy

The uncertainty of classification outcomes is of crucial importance for many safety critical applications including, for example, medical diagnostics. In such applications the uncertainty of classification can be reliably estimated within a Bayesian model averaging technique that allows the use of prior information. Decision Tree (DT) classification models used within such a technique gives experts additional information by making this classification scheme observable. The use of the Markov Chain Monte Carlo (MCMC) methodology of stochastic sampling makes the Bayesian DT technique feasible to perform. However, in practice, the MCMC technique may become stuck in a particular DT which is far away from a region with a maximal posterior. Sampling such DTs causes bias in the posterior estimates, and as a result the evaluation of classification uncertainty may be incorrect. In a particular case, the negative effect of such sampling may be reduced by giving additional prior information on the shape of DTs. In this paper we describe a new approach based on sweeping the DTs without additional priors on the favorite shape of DTs. The performances of Bayesian DT techniques with the standard and sweeping strategies are compared on a synthetic data as well as on real datasets. Quantitatively evaluating the uncertainty in terms of entropy of class posterior probabilities, we found that the sweeping strategy is superior to the standard strategy

Transthoracic echocardiography for imaging of the different coronary artery segments: a feasibility study

<p>Abstract</p> <p>Background</p> <p>Transthoracic echocardiography (TTE) may be used for direct inspection of various parts of the main coronary arteries for detection of coronary stenoses and occlusions. We aimed to assess the feasibility of TTE to visualise the complete segments of the left main (LM), left descending (LAD), circumflex (Cx) and right (RCA) coronary arteries.</p> <p>Methods</p> <p>One hundred and eleven patients scheduled for diagnostic coronary angiography because of chest pain or acute coronary syndrome had a TTE study to map the passage of the main coronary arteries. LAD, Cx and RCA were each divided into proximal, middle and distal segments. If any part of the individual segment of a coronary artery with antegrade blood flow was not visualised, the segment was labeled as not satisfactorily seen.</p> <p>Results</p> <p>Complete imaging of the LM was achieved in 98% of the patients. With antegrade directed coronary artery flow, the proximal, middle and distal segments of LAD were completely seen in 96%, 95% and 91% of patients, respectively. Adding the completely seen segments with antegrade coronary flow and segments with retrograde coronary flow, the proximal, middle and distal segments of LAD were adequately visualised in 96%, 96% and 93% of patients, respectively. With antegrade directed coronary artery flow, the proximal, middle and distal segments of Cx were completely seen in 88%, 61% and 3% and in RCA in 40%, 28% and 54% of patients. Retrograde coronary artery flow was correctly identified as verified by coronary angiography in seven coronary segments, mainly in the posterior descending artery (labeled as the distal segment of RCA) and distal LAD.</p> <p>Conclusions</p> <p>TTE is a feasible method for complete demonstration of coronary flow in the LM, the proximal Cx and the different segments of LAD, but less suitable for the RCA and mid and distal segments of the Cx. (ClinicalTrials.gov number NTC00281346.)</p

Recipes for sparse LDA of horizontal data

Many important modern applications require analyzing data with more variables than observations, called for short horizontal. In such situation the classical Fisher’s linear discriminant analysis (LDA) does not possess solution because the within-group scatter matrix is singular. Moreover, the number of the variables is usually huge and the classical type of solutions (discriminant functions) are difficult to interpret as they involve all available variables. Nowadays, the aim is to develop fast and reliable algorithms for sparse LDA of horizontal data. The resulting discriminant functions depend on very few original variables, which facilitates their interpretation. The main theoretical and numerical challenge is how to cope with the singularity of the within-group scatter matrix. This work aims at classifying the existing approaches according to the way they tackle this singularity issue, and suggest new ones

Standard survey methods for estimating colony losses and explanatory risk factors in Apis mellifera

This chapter addresses survey methodology and questionnaire design for the collection of data pertaining to estimation of honey bee colony loss rates and identification of risk factors for colony loss. Sources of error in surveys are described. Advantages and disadvantages of different random and non-random sampling strategies and different modes of data collection are presented to enable the researcher to make an informed choice. We discuss survey and questionnaire methodology in some detail, for the purpose of raising awareness of issues to be considered during the survey design stage in order to minimise error and bias in the results. Aspects of survey design are illustrated using surveys in Scotland. Part of a standardized questionnaire is given as a further example, developed by the COLOSS working group for Monitoring and Diagnosis. Approaches to data analysis are described, focussing on estimation of loss rates. Dutch monitoring data from 2012 were used for an example of a statistical analysis with the public domain R software. We demonstrate the estimation of the overall proportion of losses and corresponding confidence interval using a quasi-binomial model to account for extra-binomial variation. We also illustrate generalized linear model fitting when incorporating a single risk factor, and derivation of relevant confidence intervals

High connectivity among locally adapted populations of a marine fish (Menidia menidia)

Author Posting. © Ecological Society of America, 2010. This article is posted here by permission of Ecological Society of America for personal use, not for redistribution. The definitive version was published in Ecology 91 (2010): 3526–3537, doi:10.1890/09-0548.1.Patterns of connectivity are important in understanding the geographic scale of local adaptation in marine populations. While natural selection can lead to local adaptation, high connectivity can diminish the potential for such adaptation to occur. Connectivity, defined as the exchange of individuals among subpopulations, is presumed to be significant in most marine species due to life histories that include widely dispersive stages. However, evidence of local adaptation in marine species, such the Atlantic silverside, Menidia menidia, raises questions concerning the degree of connectivity. We examined geochemical signatures in the otoliths, or ear bones, of adult Atlantic silversides collected in 11 locations along the northeastern coast of the United States from New Jersey to Maine in 2004 and eight locations in 2005 using laser ablation inductively coupled plasma mass spectrometry (ICP-MS) and isotope ratio monitoring mass spectrometry (irm-MS). These signatures were then compared to baseline signatures of juvenile fish of known origin to determine natal origin of these adult fish. We then estimated migration distances and the degree of mixing from these data. In both years, fish generally had the highest probability of originating from the same location in which they were captured (0.01–0.80), but evidence of mixing throughout the sample area was present. Furthermore, adult M. menidia exhibit highly dispersive behavior with some fish migrating over 700 km. The probability of adult fish returning to natal areas differed between years, with the probability being, on average, 0.2 higher in the second year. These findings demonstrate that marine species with largely open populations are capable of local adaptation despite apparently high gene flow.This work was funded by the National Science Foundation (grant OCE-0425830 to D. O. Conover and grant OCE- 0134998 to S. R. Thorrold) and the New York State Department of Environmental Conservation

Dynamics of hollow atom formation in intense x-ray pulses probed by partial covariance mapping

When exposed to ultraintense x-radiation sources such as free electron lasers (FELs) the innermost electronic shell can efficiently be emptied, creating a transient hollow atom or molecule. Understanding the femtosecond dynamics of such systems is fundamental to achieving atomic resolution in flash diffraction imaging of noncrystallized complex biological samples. We demonstrate the capacity of a correlation method called "partial covariance mapping'' to probe the electron dynamics of neon atoms exposed to intense 8 fs pulses of 1062 eV photons. A complete picture of ionization processes competing in hollow atom formation and decay is visualized with unprecedented ease and the map reveals hitherto unobserved nonlinear sequences of photoionization and Auger events. The technique is particularly well suited to the high counting rate inherent in FEL experiments

Application of Linear Discriminant Analysis in Dimensionality Reduction for Hand Motion Classification

The classification of upper-limb movements based on surface electromyography (EMG) signals is an important issue in the control of assistive devices and rehabilitation systems. Increasing the number of EMG channels and features in order to increase the number of control commands can yield a high dimensional feature vector. To cope with the accuracy and computation problems associated with high dimensionality, it is commonplace to apply a processing step that transforms the data to a space of significantly lower dimensions with only a limited loss of useful information. Linear discriminant analysis (LDA) has been successfully applied as an EMG feature projection method. Recently, a number of extended LDA-based algorithms have been proposed, which are more competitive in terms of both classification accuracy and computational costs/times with classical LDA. This paper presents the findings of a comparative study of classical LDA and five extended LDA methods. From a quantitative comparison based on seven multi-feature sets, three extended LDA-based algorithms, consisting of uncorrelated LDA, orthogonal LDA and orthogonal fuzzy neighborhood discriminant analysis, produce better class separability when compared with a baseline system (without feature projection), principle component analysis (PCA), and classical LDA. Based on a 7-dimension time domain and time-scale feature vectors, these methods achieved respectively 95.2% and 93.2% classification accuracy by using a linear discriminant classifier

Objective surface evaluation of fiber reinforced polymer composites

The mechanical properties of advanced composites are essential for their structural performance, but the surface finish on exterior composite panels is of critical importance for customer satisfaction. This paper describes the application of wavelet texture analysis (WTA) to the task of automatically classifying the surface finish properties of two fiber reinforced polymer (FRP) composite construction types (clear resin and gel-coat) into three quality grades. Samples were imaged and wavelet multi-scale decomposition was used to create a visual texture representation of the sample, capturing image features at different scales and orientations. Principal components analysis was used to reduce the dimensionality of the texture feature vector, permitting successful classification of the samples using only the first principal component. This work extends and further validates the feasibility of this approach as the basis for automated non-contact classification of composite surface finish using image analysis.<br /

CHARMM: The biomolecular simulation program

CHARMM (Chemistry at HARvard Molecular Mechanics) is a highly versatile and widely used molecular simulation program. It has been developed over the last three decades with a primary focus on molecules of biological interest, including proteins, peptides, lipids, nucleic acids, carbohydrates, and small molecule ligands, as they occur in solution, crystals, and membrane environments. For the study of such systems, the program provides a large suite of computational tools that include numerous conformational and path sampling methods, free energy estimators, molecular minimization, dynamics, and analysis techniques, and model-building capabilities. The CHARMM program is applicable to problems involving a much broader class of many-particle systems. Calculations with CHARMM can be performed using a number of different energy functions and models, from mixed quantum mechanical-molecular mechanical force fields, to all-atom classical potential energy functions with explicit solvent and various boundary conditions, to implicit solvent and membrane models. The program has been ported to numerous platforms in both serial and parallel architectures. This article provides an overview of the program as it exists today with an emphasis on developments since the publication of the original CHARMM article in 1983. © 2009 Wiley Periodicals, Inc.J Comput Chem, 2009.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/63074/1/21287_ftp.pd